Regression kriging to improve basal area and growing stock volume estimation based on remotely sensed data, terrain indices and forest inventory of black pine forests

Main Article Content

Ferhat Bolat
Sinan Bulut
Alkan Günlü
İlker Ercanlı
Muammer Şenyurt

Keywords

Forestry, k-fold cross-validation, Landsat 8, Sentinel-2, semi-arid region

Abstract

Background: The use of satellite imagery to quantify forest metrics has become popular because of the high costs associated with the collection of data in the field.
Methods: Multiple linear regression (MLR) and regression kriging (RK) techniques were used for the spatial interpolation of basal area (G) and growing stock volume (GSV) based on Landsat 8 and Sentinel-2. The performance of the models was tested using the repeated k-fold cross-validation method.
Results: The prediction accuracy of G and GSV was strongly related to forest vegetation structure and spatial dependency. The nugget value of semivariograms suggested a moderately spatial dependence for both variables (nugget/sill ratio approx. 70%). Landsat 8 and Sentinel-2 based RK explained approximately 52% of the total variance in G and GSV. Root-mean-square errors were 7.84 m2 ha-1 and 49.68 m3 ha-1 for G and GSV, respectively.
Conclusions: The diversity of stand structure particularly at the poorer sites was considered the principal factor decreasing the prediction quality of G and GSV by RK.

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